<strong>Background</strong>: Nowadays, small drones are inexpensive and can be purchased and used very easily. Unfortunately, they are also relatively easy to convert to weapons. As they become more widespread, these drones may become a serious security risk. One possible way to address this threat could be the early detection of small drones by using acoustic cameras. However, the question arises as to how good the detection performance of such cameras is, compared to that of a human observer. The goal of this project was to determine the acoustic detection-threshold of human observers for drones in the presence of ambient noise. <strong>Methods</strong>: Nineteen subjects volunteered to take part in the study. The constancy method was used to determine the detection threshold. During the test, the study participants were presented with a recording of a DJI Phantom2 Vision+ drone that varied in level in steps of 1dB over a range of 27dB around the estimated threshold value. The signals were superimposed by three different kinds of ambient noise which were presented in three successive test-runs. The subjects wore headphones over which they heard the ongoing ambient noise while they were presented with the drone sound at random intervals and levels. The test signal was on for 2 seconds during which the trial subject had to confirm the detection of the drone sound by pressing an assigned key on a notebook. <strong>Results</strong>: We’ve found detection thresholds for white noise, water or highway noise at -17dB, -18dB and -17dB respectively, expressed as level differences between test signal and noise. Comparison of our results with the detection performance of human observers in a simulated drone detection scenario, reproduced by loudspeakers in an anechoic chamber, showed good agreement. Further, it seems possible to assess the detection performance of an acoustic camera using our results.
Background: In target detection, the success rates depend strongly on human observer performances. Two prior studies tested the contributions of target detection algorithms and prior training sessions. The aim of this Swiss-German cooperation study was to evaluate the dependency of human observer performance on the quality of supporting image analysis algorithms. Methods: The participants were presented 15 different video sequences. Their task was to detect all targets in the shortest possible time. Each video sequence showed a heavily cluttered simulated public area from a different viewing angle. In each video sequence, the number of avatars in the area was altered to 100, 150 and 200 subjects. The number of targets appearing was kept at 10%. The number of marked targets varied from 0, 5, 10, 20 up to 40 marked subjects while keeping the positive predictive value of the detection algorithm at 20%. During the task, workload level was assessed by applying an acoustic secondary task. Detection rates and detection times for the targets were analyzed using inferential statistics. Results: The study found Target Detection Time to increase and Target Detection Rates to decrease with increasing numbers of avatars. The same is true for the Secondary Task Reaction Time while there was no effect on Secondary Task Hit Rate. Furthermore, we found a trend for a u-shaped correlation between the numbers of markings and RTST indicating increased workload. Conclusion: The trial results may indicate useful criteria for the design of training and support of observers in observational tasks.
The last few years showed that a high risk of asynchronous threats is given in every day life. Especially in large crowds a high probability of asynchronous attacks is evident. High observational abilities to detect threats are desirable. Consequently highly trained security and observation personal is needed. This paper evaluates the effectiveness of a training methodology to enhance performance of observation personnel engaging in a specific target identification task. For this purpose a crowd simulation video is utilized. The study first provides a measurement of the base performance before the training sessions. Furthermore a training procedure will be performed. Base performance will then be compared to the after training performance in order to look for a training effect. A thorough evaluation of both the training sessions as well as the overall performance will be done in this paper. A specific hypotheses based metric is used. Results will be discussed in order to provide guidelines for the design of training for observational tasks.
Background: Algorithms show difficulties in distinguishing weak signals of a target from a cluttered background, a task that humans tend to master relatively easily. We conducted two studies to identify how various degrees of clutter influence operator performance and search patterns in a visual target detection task.
Methods: First, 8 male subjects had to look for specific female targets within a heavily cluttered public area. Subjects were supported by differing amounts of markings that helped them to identify females in general. We presented video clips and analyzed the search patterns. Second, 18 subject matter experts had to identify targets on a heavily frequented motorway intersection. We presented them with video material from a UAV (Unmanned Aerial Vehicle) surveillance mission. The video image was subdivided in three zones: The central zone (CZ), a circle area of 10°. The peripheral zone (PZ) corresponding to a 4:3 format and the hyper peripheral zone (HPZ) which represented the lateral region specific to the 16:9 format. We analyzed fixation densities and task performance.
Results: We found an approximately U-shaped correlation between the number of markings in a video and the degree of structure in search patterns as well as performance. For the motorway surveillance task we found a difference in mean detection time for CZ vs. HPZ (p=0.01) and PZ vs. HPZ (p=0.003) but no difference for CZ vs. PZ (p=0.491). There were no differences in detection rate for the respective zones. We found the highest fixation density in CZ decreasing towards HPZ.
Conclusion: We were able to demonstrate that markings could increase surveillance operator performance in a cluttered environment as long as their number is kept in an optimal range. When performing a search task within a heavily cluttered environment, humans tend to show rather erratic search patterns and spend more time watching central picture areas.